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CodebaseChat
✓ verifiedFreemium
Connects Git repos to answer plain-English questions about your code with file references and dependency context.
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✓ verifiedFreemium
Open-source AI coding assistant offering autocomplete and chat in IDEs; the company was acquired by Cursor.
775K visits/mo
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Released
✓ verifiedFreemium
Jira-native tool that turns existing issues into customer roadmaps, release notes, and feedback portals without duplicate data entry.
11K visits/mo941 saves
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Workik
✓ verifiedFreemium
AI coding assistant that gathers project context to plan, generate, test and ship code across the SDLC via IDE and chat integrations.
210K visits/mo
Pricing
Starter: $0 (1 repo, 50 questions/mo)
Pro: $12/mo (10 repos, unlimited questions)
Team: $49/mo (unlimited repos, SSO)
No public pricing
Free: $0/mo (up to 10 users, 2,000 AI tokens/user)
Standard: $1.10/user/month (unlimited users, 10,000 AI tokens/user)
Advanced: $1.70/user/month (unlimited users, 20,000 AI tokens/user)
Free trial available
Trial: $0/month (20 AI requests on signup, 7 requests/day, 50 free flow runs, up to 3 users)
Starter: $15/month billed annually or $25/month (20M standard AI tokens, 1,000 flow runs)
Premium: $30/month billed annually or $50/month (40M standard + 4M advanced AI tokens, 3,000 flow runs)
Elite: $80/month billed annually or $150/month (100M standard + 10M advanced AI tokens, 10,000 flow runs)
Tailored: $62/month (custom AI token allocation)
Free trial available
Core features
- ✦Natural-language search across a codebase
- ✦Architecture explanations and dependency graphs
- ✦Bug hunter that traces issues across files
- ✦AI code review before opening a PR
- ✦Automatic documentation generation
- ✦Multi-repo support via OAuth
- ✦Open-source AI code assistant
- ✦Customizable autocomplete
- ✦In-editor AI chat
- ✦Community-built coding agent
- ✦Roadmaps synced live from Jira issues
- ✦AI-generated release notes
- ✦Customer feedback and idea portals
- ✦Audience-specific roadmap views
- ✦Password-protected or invite-only sharing
- ✦Publishing to Confluence and Slack
- ✦Automatic context-gathering from connected engineering sources
- ✦AI-generated code, tests and pull requests from tickets
- ✦Task planning that breaks complex work into subtasks
- ✦Auto-updating engineering documentation
- ✦Vector search over embedded project data
- ✦Multiple selectable AI models (GPT, Gemini, Claude, Llama, etc.)
- ✦Engineering productivity analytics dashboard
Use cases
- →Onboarding new engineers faster
- →Answering questions about a codebase
- →Understanding how components connect
- →Finding and diagnosing bugs
- →Generating documentation from code
- →Get AI code completions while coding
- →Ask questions about code in the editor
- →Build on an open-source coding-agent foundation
- →Sharing a public product roadmap with customers
- →Publishing release notes automatically from Jira tickets
- →Collecting and prioritizing customer feature requests
- →Giving executives a curated view of product progress
- →Engineering teams automating ticket-to-PR workflows
- →Developers wanting AI-assisted debugging and test generation
- →Engineering managers tracking AI-driven productivity gains
- →Teams centralizing documentation from scattered sources
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